{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FkWAPpaMX6FB"
   },
   "source": [
    "\n",
    "In this section, we will load a dataset, do exploratory data analysis (EDA) such as visualizing the distributions. We do typical preprocessing tasks such as encode categorical variables and normalize, and rescale for neural network training. We will then create a simple neural network model in keras and train the model plotting in offline fashion and using a generator, and plot the training and validation performance.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yWitkfdDBOR0"
   },
   "source": [
    "In this recipe, we are going to look at a still quite simple dataset, the [adult dataset](http://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI machine learning repository](http://archive.ics.uci.edu/ml/datasets). With this dataset (also known as Census Income dataset), the goal is to predict from census data, whether someone earns more than 50,000 dollars.\n",
    "\n",
    "This dataset is already split into training and test. We will start by loading training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "V57_75MXCLNN",
    "outputId": "588cdfee-d662-4dd2-d712-3b5aaf1159b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-12-10 20:33:09--  http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3974305 (3.8M) [application/x-httpd-php]\n",
      "Saving to: ‘adult.data’\n",
      "\n",
      "adult.data          100%[===================>]   3.79M  3.59MB/s    in 1.1s    \n",
      "\n",
      "2019-12-10 20:33:11 (3.59 MB/s) - ‘adult.data’ saved [3974305/3974305]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "T0xod3gaCNSl",
    "outputId": "463d970f-ce83-46e7-f6a3-3f9f86f1518c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass  fnlwgt  ... hours-per-week  native-country     50k\n",
       "0   39          State-gov   77516  ...             40   United-States   <=50K\n",
       "1   50   Self-emp-not-inc   83311  ...             13   United-States   <=50K\n",
       "2   38            Private  215646  ...             40   United-States   <=50K\n",
       "3   53            Private  234721  ...             40   United-States   <=50K\n",
       "4   28            Private  338409  ...             40            Cuba   <=50K\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "train = pd.read_csv(\n",
    "    'adult.data',\n",
    "    names=['age', 'workclass', 'fnlwgt',\n",
    "             'education', 'education-num',\n",
    "             'marital-status', 'occupation',\n",
    "             'relationship', 'race', 'sex',\n",
    "             'capital-gain', 'capital-loss',\n",
    "             'hours-per-week', 'native-country', '50k']\n",
    "             )\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "C0wBBWQMDY3d",
    "outputId": "5e68b1d2-53db-4809-e8f8-553a310ec5c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      "age               32561 non-null int64\n",
      "workclass         32561 non-null object\n",
      "fnlwgt            32561 non-null int64\n",
      "education         32561 non-null object\n",
      "education-num     32561 non-null int64\n",
      "marital-status    32561 non-null object\n",
      "occupation        32561 non-null object\n",
      "relationship      32561 non-null object\n",
      "race              32561 non-null object\n",
      "sex               32561 non-null object\n",
      "capital-gain      32561 non-null int64\n",
      "capital-loss      32561 non-null int64\n",
      "hours-per-week    32561 non-null int64\n",
      "native-country    32561 non-null object\n",
      "50k               32561 non-null object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l3ny_HFQy4IA"
   },
   "source": [
    "We have the following information from the UCI description page: \n",
    "* age: continuous.\n",
    "* workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "* fnlwgt: continuous.\n",
    "* education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "* education-num: continuous.\n",
    "* marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "* occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "* relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "* race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "* sex: Female, Male.\n",
    "* capital-gain: continuous.\n",
    "* capital-loss: continuous.\n",
    "* hours-per-week: continuous.\n",
    "* native-country: United-States, etc.\n",
    "\n",
    "\"fnlwgt\" actually stands for the final weight, i.e. the total number of people constituting the entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "bUrTvz6-kw9h",
    "outputId": "e69d90e9-b552-4724-fe8a-12a172c3af90"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['workclass', 'education', 'marital-status', 'occupation',\n",
       "       'relationship', 'race', 'sex', 'native-country', '50k'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.select_dtypes(include='object').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7G9CaiB94_iq"
   },
   "source": [
    "We cannot do much with string encoding, unfortunately. As a first step we are going to transform these variables using Ordinal Encoding. We can do this very flexibly using the *category_encoding* package in *sklearn-contrib*.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "emJ7vHnTkPJq",
    "outputId": "88666b5d-da8f-4462-a818-f14ecc12105b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting category_encoders\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a0/52/c54191ad3782de633ea3d6ee3bb2837bda0cf3bc97644bb6375cf14150a0/category_encoders-2.1.0-py2.py3-none-any.whl (100kB)\n",
      "\r",
      "\u001b[K     |███▎                            | 10kB 15.4MB/s eta 0:00:01\r",
      "\u001b[K     |██████▌                         | 20kB 1.9MB/s eta 0:00:01\r",
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      "\u001b[K     |████████████████▍               | 51kB 2.1MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████▋            | 61kB 2.5MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████████▉         | 71kB 2.9MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████████████▏     | 81kB 3.3MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████████████████▍  | 92kB 3.7MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████████| 102kB 2.4MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.17.4)\n",
      "Requirement already satisfied: pandas>=0.21.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.25.3)\n",
      "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (1.3.3)\n",
      "Requirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.5.1)\n",
      "Requirement already satisfied: statsmodels>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.10.2)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.6/dist-packages (from category_encoders) (0.21.3)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.21.1->category_encoders) (2.6.1)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.1->category_encoders) (1.12.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.20.0->category_encoders) (0.14.0)\n",
      "Installing collected packages: category-encoders\n",
      "Successfully installed category-encoders-2.1.0\n"
     ]
    }
   ],
   "source": [
    "pip install category_encoders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "erM0qk-YmmLd",
    "outputId": "e8f84cfc-7a4e-4f20-b261-5694e8c6211f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>77516</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
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       "      <td>2174</td>\n",
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       "      <td>83311</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>3</td>\n",
       "      <td>215646</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>3</td>\n",
       "      <td>234721</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>338409</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt  ...  capital-loss  hours-per-week  native-country\n",
       "0   39          1   77516  ...             0              40               1\n",
       "1   50          2   83311  ...             0              13               1\n",
       "2   38          3  215646  ...             0              40               1\n",
       "3   53          3  234721  ...             0              40               1\n",
       "4   28          3  338409  ...             0              40               2\n",
       "\n",
       "[5 rows x 14 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import category_encoders as ce\n",
    "\n",
    "X = train.drop('50k', axis=1)\n",
    "encoder = ce.OrdinalEncoder(cols=list(X.select_dtypes(include='object').columns)[:])\n",
    "encoder.fit(X, train['50k'])\n",
    "X_cleaned = encoder.transform(X)\n",
    "\n",
    "X_cleaned.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BrhJ8kSTnqyb"
   },
   "source": [
    "When starting with a new task, the best is often to examine the data. Let us have a look at the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FaPUElVj5IMi"
   },
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "from matplotlib import pyplot\n",
    "a5_dims = (8.27, 11.7 / 2)\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.5, rc={\"lines.linewidth\": 2.0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "id": "X41KGK-156wJ",
    "outputId": "205e19e0-ea2d-4061-a2e8-4216e1c3f98c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OLjWpXqu20uVUuvZdHsO9TgOSHsTx29kjSpcjhEWRsDAjhi6opq0s5nrFX6nVavoPfQqA\nk4d2kqWV6VeFqCwSFmYk7+K2pV2v+KsWbbtRp14jHiY/4MLpQ0qXI4TFkLAwE3q9/i/XK1orXI1y\nVCoV/YdNBOCnw9+SmZmucEVCWAYJCzMRdy+S1JRknJyrU8OtjtLlKMqnZUfqefuQmpLMuVM/KF2O\nEBZBwsJMGLqgmra2yOsVf6VSqRgwbBIAp4/uISM9VeGKhKj6JCzMRNgNuV7xV4182uLdpBXpaSmc\nOfG90uUIUeVJWJgBvV5PeKhcr/ir3GsXuWcXvxzfR1rqQ4UrEqJqk7AwA/fjokl9mISjkys13esq\nXY7J8G7cksbN25OZkcbpY3uVLkeIKk3CwgxEhIUAUL9hM4u/XvF3A/64M+rcyf2kPExUuBohqi4J\nCzMQEZ4bFvW8mylciemp5+VDs9ad0Woz+PnIbqXLEaLKkrAwA5Hh14HcMwtRUP+huWcX538+SHJS\ngsLVCFE1SViYuMyMdGKi76BWW1G3fmOlyzFJdeo1pFX77mRnaTn14zdKlyNElSRhYeKi7txAr9dR\n29MbjcZW6XJMVr8hT6FSqbj4y2FiY+4pXY4QVY6EhYmLkC6oYnGv04A2HXuTk5PNjm2blC5HiCpH\nwsLEGe6EkovbRvXzm4Barebojz9wP+6u0uUIUaVIWJgwvV5vuLhdz9tH4WpMX033urTr3BedLocT\nB3coXY4QVYqEhQlLiL9HWmoy1ZxcqF7TQ+lyzEJfvwlYW1sT+Osp4u5FKF2OEFWGhIUJ+2sXlDyM\nVzzVa7ozcPBw9Hodxw98rXQ5QlQZEhYmLO9hvKpwvUKlVpGamV2il05fum2Ne8ofa2sbgi6d5l5U\neLnuhxCWSmaoN2FV6XpFZlYOAdfjStSmnY9bqbZVy82dTj0Hc/bk9xz7YRuTZiwv1ecIIf4kZxYm\nSpuZQUx0OGq1Gs8GTZQux+z0HvQkNhpbrl05T9Sdm0qXI4TZk7AwUdGRt9DpdLjX8UJja6d0OWbH\nybk6XXsPBeDo/q0KVyOE+ZOwMFHRf/w27OklZxWl1WvgKGxt7bkZfInbt4KVLkcIs1assNBqtbz3\n3nv06tWLtm3bMn78eM6cOVOsDcTExLBw4UI6deqEr68vc+fOJSIi/y2Nd+/eZc2aNYwdO5bOnTvT\ntWtXpkyZUuxtVEXREaEAeNaXsCgth2rOdOs7HIBjcnYhRJkUKyyWLVvGpk2bGDFiBCtWrECtVjNj\nxgwuXbr0yHapqan4+/tz8eJFZs+ezYIFC7h69Sr+/v4kJSUZ1jt69CifffYZXl5evPDCC8ydO5fU\n1FSmTp3Knj17yraHZir6Tm5YFHfwwMq828ic9Og3Ejv7aoTdCOLW9UClyxHCbBm9GyowMJD9+/ez\nfPlypk6dCsCoUaMYPnw4K1euZMuWLUW23bp1K7dv32bXrl20bNkSgN69e/PEE0+wceNGFi5cCEDX\nrl05fvw4NWrUMLSdOHEiI0eOZPXq1YwaNaos+2h2UlNTiY+NwsrKGvc6DYrVpjLvNjIn9g7V6Nl/\nJEf3b+Xo/m00bNpGnlkRohSMnlkcPHgQGxsbxo0bZ1hma2vL2LFjuXjxIrGxsUW2PXToEO3btzcE\nBUDjxo3p3r07Bw4cMCxr2rRpvqAA0Gg09OnTh6ioKDIyMkq0U+Yu9GbuLbMedb2wtrFRuBrz163P\ncByqORMRdo2bwY8+GxZCFM5oWAQHB9OwYUOqVauWb3nbtm3R6/UEBxd+4VCn0xESEkLr1q0LvNem\nTRvCw8NJT09/5Lbj4uJwcHDA1tY8h+bO1lHsLqHYhDTD369fzz2mdRvI/BXlwdbOnl4DRwNw9Idt\n6PUW0P8mRDkz2g0VFxeHh0fBcYnc3HK7MIo6s0hMTESr1RrW+3tbvV5PXFwcDRoU3s1y+/ZtDh8+\nzLBhw8y22yAzK5sLwTHFWtfJ0Y6HKblnUDeuXwPk4nZ56tJ7CL8c30f0nZtcu3KeFm27Kl2SEGbF\naFhkZGRgU0hXSN5v+5mZmYW2y1uu0WiKbFtU91J6ejoLFy7E3t6eRYsWGSuxUDVrOpaqXXnSJ6Th\n5Fj8ZyTy1r15I3eYjybNWhS7vY2NdYm2Vdo2lbmtwtoU5zMK35Ydg4ZPYPfW/3Li4HY6deuNWv3n\nibWDgy1uNRxKVJ8pc3NzUroEkyfHqGSMhoWdnR1ZWVkFlueFQVFdRHnLtVptkW3t7Ar+w8/JyWHR\nokWEhoby+eef4+7ubqzEQt2/n4JO4dt90jKzDWcLxuSdWWSkpxIdFYG1tQ3VXDyK3T4rq/jbKkub\nytzW39v89eyrNNtq07E/xw58w93IcM79fJzWvj0N76WlZRKXk1Oi+kyVm5sTcXEPlS7DpMkxKkit\nVj3yl2yj1yzc3NwK7WqKi8u986aoH+aurq5oNBrDen9vq1KpCu2i+uc//8nJkyd555136NKli7Hy\nqpzoiFvAHxe3reXidnmytrGhz+O5N2ocO7ANna5qhIMQlcFoWDRv3pywsDBSU1PzLQ8ICDC8X+gH\nq9X4+PgQFBRU4L3AwEC8vLywt7fPt/ydd95h165dvPTSSwwdOrTYO1GVREfkPrldV8aDqhAduvWn\nek0P4mOiCPz1J6XLEcJsGA0LPz8/srKy2Llzp2GZVqtl165d+Pr6Gi5+R0dHExoamq/t4MGDuXz5\nMlevXjUsu3XrFmfPnsXPzy/fup999hlffPEFs2fPZsqUKWXaKXOW9zCeZzEfxhMlY2VlTV+/8QAc\nP7CdnJxshSsSwjwYvWbRrl07/Pz8WLlypeHupd27dxMdHc1bb71lWG/p0qWcP3+ekJAQw7JJkyax\nc+dOZs6cybRp07CysmLjxo24ubkZHvADOHz4MO+99x7e3t40atSIvXv35qth0KBBODhUnYuPj5I3\nzIfcNltx2nbqw0+HdxEfG8Xlc8fp2GOQ0iUJYfKKNZ/Fu+++y0cffcTevXtJSkqiWbNmrF+/no4d\nOz6ynaOjI5s3b+bNN99k3bp16HQ6unbtyooVK6hevbphvWvXcm8VDQ8PZ8mSJQU+5+jRoxYRFulp\nKSTE30Oj0eBWu77S5VRZVlZW9BsygZ2bPuDEoR2069xX6ZKEMHnFCgtbW1uWLl3K0qVLi1xn8+bN\nhS6vXbs2q1evfuTnP//88zz//PPFKaVKuxuZe3Hbu1ETrKxkXqqK1KpDT07++A2xd+9w8cxhured\npnRJQpg0GaLchNyNDAOgUWPznxnP1KnVavoPnQjAqR+/KfJ5ISFELgkLE5I3X3TDRnInVGVo0bYr\ndeo14mHyA/Z/t0vpcoQwaRIWJuReVO6ZRcPGTRWuxDKoVCoGDJsEwNfbN5OWlmqkhRCWS8LCRGRl\naYm7F4lKpcbbW+6EqixNW/pSv2FzkpMS2bbtf0qXI4TJkrAwEfei76DT5VDTvQ62hQyDIiqGSqVi\n8MhnAPj66y3Ex5dsThAhLIWEhYnIexivjmdDhSuxPA0aNadnr75kZGTw+eeflqhtSYahz3tl6ypo\nR4SoQHJ/pomIupN722xtCQtFTHtuDmfP/MSBA98zbtxEGjUqXldgSYahz9O5hQfWtvJPT5gXObMw\nEVF/DCBYu56EhRI869Vn5Mgn0el0/Pe/a5UuRwiTI2FhAnQ6HVHSDaW4qVOn4+BQjbNnT3Px4gWl\nyxHCpEhYmIDEhFgyM9JxdK6Oo7Or0uVYLFfX6kye7A/AJ5+sQaeTiwtC5JGwMAF5z1fU9vRWthDB\nuHETcXNz5/r1axw58qPS5QhhMiQsTEDeMB/SBaU8Ozs7nntuFgAbNqyTYUCE+IOEhQnIG+ZDLm6b\nhsGDh9KoURNiYu6xa9cOpcsRwiRIWJiAvG4oObMwDVZWVsydmzsK8ubNX5KUlKhwRUIoT8JCYWmp\nySQ9iEejsaWGW22lyxF/6Ny5G506dSUlJYUvvtigdDlCKE7CQmF5XVB16nmjVlspW4wwUKlUzJ//\nAmq1mr17v+XWrVDjjYSowiQsFHYv6jYAdeR6hclp1KgxI0eOQafTsWbNB+j1eqVLEkIxEhYKi72b\nFxbeyhYiCvXsszNwcnLm4sUL/PzzKaXLEUIxEhYKi4nODYu69eXMwhS5uLjy7LMzAFi3bhVarVbh\nioRQhoSFgnQ6HbH3IgC5E8qUjRw5Bm/vhkRFRfLNN9uVLkcIRUhYKOjB/RiytJk4udSgmqOT0uWI\nIlhbWzN//iIANm36gvv34xWuSIjKJ2GhoLwuKI86XgpXIozp0qUbPXr0Jj09jfXr1yldjhCVTsJC\nQYawqNtA4UqESq0yOmnRszPmY21tzYED33Pht0ukZmajkxukhIWQGVgUlHcnlEddObNQWmZWDgHX\njU2pqqFH/5Gc+vFb3nvvLWYtXolvC3mQUlgGObNQUEz0HUC6oczJY4+Pw7WGG/eiwrnw80GlyxGi\n0khYKCRLm8n9uLuo1Wpq1a6ndDmimDQaW4Y8+RwAR/dv5UHCfYUrEqJySFgoJC4mEr1eRw23OtjY\naJQuR5RA8zZd8GnVkcyMNDZ+Jhe7hWWQsFCI3AllvlQqFUPHTMfaRsOJY4cIuxGkdElCVDgJC4XE\n3P3jeoVc3DZLNWrVpvfAJwHY/816cnKyFa5IiIolYaGQP2+blbAwV70GjqZ2HU9i70bwy7F9Spcj\nRIWSsFBIrKEbSp6xMFc2NhrmPL8YgOMHv+Z+3F2FKxKi4khYKCAtNZmHyQ/QaOxwremhdDmiDNr7\ndqZd575kZ2nZ9/UnMoy5qLIkLBSQ93yFW536qNXyv8Dc+Y2ehkM1Z8KuX+HSuWNKlyNEhZCfVAqQ\nO6GqlmqOzgx58lkADu3ZSEqyzNktqh4JCwXE3JUxoaqatp0eo0nzDqSnpfDDrs+VLkeIcidhoQC5\nE6rqUalUPDFhNjYaW4J++5lrVy4oXZIQ5UrCopLpdDpi78qYUFVR9ZruDBg2CYDvd3xKelpqoesV\nZ4Tbv7+ydZW5J0IUJKPOVrKkB3FoMzNwdHKlmpOL0uWIctatzzCu/PYzUbdvcHD3F4ye/HyBdYo3\nwm1+nVt4YG0r/1yFcuTMopLldUG5SxdUlaRWW/Hk5Oextrbh0rljhARJd5SoGiQsKplhmA95GK/K\ncqtdnwHDJwOwd/snpKUmK1yREGUnYVHJYuXitkXo3nc4DRq1ICX5Afu/+UzpcoQoMwmLShYjs+NZ\nhLzuKBuNLVcu/sTvl88oXZIQZSJhUYmys7OIj4lGpVLhVru+0uWIClbDrQ6Pj/AH4Luv/0vKQ3lY\nT5gvCYtKFB8ThU6XQ41atdFobJUuR1SCzr38aOjThrTUZPZuWydjRwmzJWFRieRhPMujVqsZPel5\n7OyrERJ0QebtFmZLwqIS5V2vcJeH8SyKaw03Rjw1B4CDezZyO/yWwhUJUXISFpUob7RZObOwPK07\n9MS320Cys7S8//YrZGkzlS5JiBKRsKhEsTKAoEUbMuZZarrX5Xb4LX7c95XS5QhRIhIWlSQ9LZWk\nB/HY2GioUau20uUIBdja2jPumRextrbm3Kkf5OluYVYkLCpJ3lmFW+36qNVWClcjlFK3fmOmTJsF\nwO4ta0l6EK9wRUIUT7HCQqvV8t5779GrVy/atm3L+PHjOXOmeA8ZxcTEsHDhQjp16oSvry9z584l\nIiKiwHqffPIJc+bMoWfPnjRr1ow1a9aUbE9MnFyvEHlGjJ5Ak+YdSEtNZsfG98nJyVa6JCGMKlZY\nLFu2jE2bNjFixAhWrFiBWq1mxowZXLp06ZHtUlNT8ff35+LFi8yePZsFCxZw9epV/P39SUpKyrfu\nRx99RGBgIC1atCj93piwmOhwANxlTCiLp1arGTNlIc6uNYkIu8aPe+X6hTB9RsMiMDCQ/fv3s3jx\nYpYsWcKECRPYtGkTderUYeXKlY9su3XrVm7fvs369euZPn06U6dO5fPPPycmJoaNGzfmW/fo0aOc\nPn2aDz74oEw7ZKpkmA/xV9WcXBg/bTFqtRVnTnxH0KXTSpckxCMZDYuDBw9iY2PDuHHjDMtsbW0Z\nO3YsFy9eJDY2tsi2hw4don379rRs2dKwrHHjxnTv3p0DBw7kW7devXqlqd8s6PX6Pyc8krAQf2jQ\nsDl+o6cCsGfrWuJjopQtSIhHMBoWwcHBNGzYkGrVquVb3rZtW/R6PcHBwYW20+l0hISE0Lp16wLv\ntWnThvDwcNLT00tZtnlJekFPQp4AACAASURBVBBPRnoa1RydcXRyVbocYUK6PjaM1h16os3MYPsX\n76DNzFC6JCEKZTQs4uLicHd3L7Dczc0NoMgzi8TERLRarWG9v7fV6/XExZVstjBzZXhyu64XKpVK\n4WqEKVGpVIycOI9a7p7E3o1g73YZP0qYJqPzNGZkZGBjY1Ngua1t7kB4mZmFP4mat1yj0RTZNiOj\n4n6LqlnTscI+u7j0CWk4OdqRGB8NQP0GjXBytCty/bz3bGysH7leYSqrTWVuq7A2xfkMczsWTo52\nPPv8v/jo9Re4cvEnGng3YeCw8fnaODjY4lbDodjbcHNzKlFNlkiOUckYDQs7OzuysrIKLM8Lg7wf\n/H+Xt1yr1RbZ1s6u5P84i+v+/RR0OmV/Q0vLzOZhSgZ3wm8CUN2tHg9TCg9IJ0c7w3tZWdlFrleU\nympTmdv6e5u/HiNTqK88t+Xo4sGYKS+w7bO3+WHXRlxq1KF5m86G99PSMonLySnW57u5OREX97BE\nNVkaOUYFqdWqR/6SbbQbys3NrdCuprwupMK6qABcXV3RaDSFdjXFxcXlzulQSBdVVfTnaLNy26wo\nWou2XRkwfDJ6vZ5vvvrA8L0RwhQYDYvmzZsTFhZGampqvuUBAQGG9wv9YLUaHx8fgoKCCrwXGBiI\nl5cX9vb2panZrOROeBSFSqXCvbaEhXi0xwaNoU3H3mgzM9iy/k1SU2T+bmEajIaFn58fWVlZ7Ny5\n07BMq9Wya9cufH198fDwACA6OprQ0NB8bQcPHszly5e5evWqYdmtW7c4e/Ysfn5+5bUPJu1+bDQ6\nXQ7Va9VGY1tx3W6ialCpVIyaOI+6DZqQmBDL11+8S3Z2wW5gISqb0WsW7dq1w8/Pj5UrVxIXF0eD\nBg3YvXs30dHRvPXWW4b1li5dyvnz5wkJCTEsmzRpEjt37mTmzJlMmzYNKysrNm7ciJubG1OnTs23\nnT179hAdHW24nnHhwgXWrVsHwJQpU3ByMs+LUYYuKHlyWxSTjcaWSdOX8enKfxB+83f2bV9Ht9Zv\nKF2WsHBGwwLg3Xff5aOPPmLv3r0kJSXRrFkz1q9fT8eOHR/ZztHRkc2bN/Pmm2+ybt06dDodXbt2\nZcWKFVSvXj3fut9++y3nz583/Pe5c+c4d+4cACNGjDD/sJCH8UQJOLvWZNLMl/hy9b+4fP4Em778\nlHlz5itdlrBgxQoLW1tbli5dytKlS4tcZ/PmzYUur127NqtXrza6jaLam7t7hjMLCQtRMp4NmjD+\n2cVsXf8mX2/7inp16zBy5BilyxIWSoYor2CxMiaUKAOflh0ZMSF3StYPP3yPI8ePkZqZ/chXbEKa\n4e/ZOoV3QFQZxTqzEKWTkvKQpAfxWNtoqOEmEx6J0vHtPhB7q3S2/e8L3nr930yd/yr1GzYrcv2/\nPo/SuYUH1rbyz1yUnZxZVKDw8FsAuMuER6KMJkyeRsfuA8nK0vK/T18v9jMYKrXK6JnI319yNiIK\nI79yVKDwsNxbiaULSpSVSqVi+PjZpD5M4lrQBTZ+/DLPLXiDWh6ej2yXmZVDwPWSjcEmZyOiMHJm\nUYHywkImPBLlwcrKinHTFtO4WTtSHyaxce3LPLgfo3RZwkJIWFSgsFtyZiHKl42NhokzluPVuCXJ\nSffZuPZlmcdbVAoJiwqi1+u5/cc1CwkLUZ40Glsmz1yBp1dTHtyPYdPHr5CSnKh0WaKKk7CoILGx\nMaSmpsiER6JC2Nk7MGX2v/Co6018bBRfrPknyYn3lS5LVGESFhXk1q3cYcllwiNRURyqOfHMvJdz\nAyMmis9XrSAxoehpjoUoCwmLCpI3qKI8uS0qkqOTK9Oef5W69Rvz4H4Mn6/6J3Ex0UqXJaogCYsK\nkndmIdcrREVzqObE1Pn/ob53M5IexLH27X8QFxOpdFmiipGwqCChoXlhIbfNiopnZ18N/7kv492k\nFclJCXyxagVRd24qXZaoQiQsKoBWqyUi4rZMeCQqla2dPU/P/hfNW3ckNSWZL1f/i4sXzihdlqgi\nJCwqQFjYLbKzs/GsV18mPBKVSqOx5bkFL9Ouc1+02gxef3kZl84dU7osUQVIWFSAGzdyJ4Bq3NhH\n4UqEJbK2tuHJpxfQe9AYdLocdm9Zw8lDO9Hr9UqXJsyYhEUFuHHjOgBNmhY9MqgQFUmlUjHoiaeZ\nOXcRKpWKo/u3snfbOpmiVZSahEUFMJxZNJEzC6GsYSPGMOHZf2Bto+G3s0fYuPZlUh7K096i5CQs\nyllOTg43b+aeWUhYCFPQsl13nlv4Bs4uNblzK5hPVy7hbmSY0mUJMyNhUc4iIyPIyMjA3d0DZ2cX\npcsRAsidonXW4nep5+1D0oM4PvtwGUGXTitdljAjEhblLK8LqqlcrxAmxsmlBtOef40OXfuTlaVl\nx5crObj7S3JyspUuTZgBCYtylhcWPj4SFsL02NhoGDVpPn6jn0WttuKX4/tkTClRLBIW5SzvTig5\nsxCmSqVS0aPfEzy78HVcqtciMvw6n7z7f1y7ckHp0oQJk7AoR3q9nuvXrwESFsL0NWjYnDlL3qdp\ny46kp6WwdcObHNj1BVptptKlCRMkYVGOYmNjSE5OxsXFBXd3d6XLEcIoh2rOTJ75Eo+P9EetVnPm\nxHc8P/dZrl8PUbo0YWIkLMpR3j+wpk2byxwWwmyo1Wp6DRjNcy+8RU33uty5HcasWVP57IvPSE7L\nIDUz2+grW6f0XoiKZq10AVXJn3dCyfMVwvzU9/ZhzpIPuHRqJ/v3fctXG9dz7MQJRk+aj1vt+o9s\n27mFB9a28uOkKpMzi3KUd3Fb7oQS5kqjsWXm3EX4z30ZZ5eaRIZfZ907L3Lsh21kZWmVLk8oSMKi\nHMnFbVFVNGnennnLP6Jjj0Hk5GRz4uAOPnnnRcJuBCldmlCIhEU5efAggbi4WOztHahX79Gn7EKY\nA3sHR0Y+NZdnF75BLQ9P4mOj+HLNv9i9ZQ0pyTK+lKWRsCgnwcG/A9C8eQvUajmsourwbtySuUs+\npN+Qp7CysubSuWOsem0uPx3ZTXaWjGJrKeSnWjm5ejUvLFoqXIkQ5c/axoZ+QyYwb/lH+LTqRGZm\nOof3fcXatxYQHHhO5sqwABIW5SQvLFq2bK1wJUJUnFrunjw9awX+c17GrXZ9EuLvse2zt1nyf3MJ\nDLysdHmiAklYlAOdTse1a3lh0UrhaoSoeE1atGfu0g8YNnYG9g6OBF0JYP78mSxZskge6Kui5Mbo\nchARcYeUlBRq1XLDzU2e3BaWwcrKmq6PDaVd5z6EXznG7m+3c/bsac6ePU2/fgPw93+Wxo2bKl2m\nKCdyZlEOrl7NvZ2wRQs5qxCWx86+GlOemc7XX+9m/PhJaDQajh8/yrRpk1m27EWCggKVLlGUAwmL\ncpAXFtIFJSyZq2t15s9/ga1bv2Xs2KewtbXll19+Zu7c6SxYMJuzZ39Bp5NxQcyVhEU5yLuw16ZN\nO4UrEUJ57u4eLFjwIjt37mPKlGk4Ojpy+fJvLFnyAlOmjOfbb3eQlpamdJmihCQsyigpKZGwsFto\nNBq5bVaIv3B1rc6MGXPYsWMfs2fPx93dg4iIO6xatZIxY4axdu2H3L4drnSZopgkLMroypXc/tgW\nLVqh0WgUrkYI0+Po6MikSf5s376bV199i7Zt25GamsqOHduYMmU8c+dOZ//+fXK2YeLkbqgyyuuC\natu2vcKVCGHarK2t6dt3AH37DiAkJJi9e3dx9OhhgoICCQoKZPXqD+jffyDDho2gVas2Msy/iZGw\nKKO8sGjXTsJCiOJq1qwFS5asYP78RZw4cZT9+/dx5UoA+/fvY//+fdSpU5d+/QbSv/8gmjb1keAw\nARIWZZCenk5ISDBqtZpWrdooXY4QZsfBwYGhQ59g6NAnuH07nB9+2MehQwe4ezearVu/YuvWr6hX\nr74hOBo1aizBoRAJizK4fPk3cnJyaN68JdWqOSpdjhCKUalVpGZml7idrY011n9cOfXy8mbOnAXM\nnDmPK1cCOHbsMCdOHCMyMoLNm79k8+YvqVvXk+7de9GjRy/atesg1wkrkYRFGZw/fxaArl27K1yJ\nEMrKzMoh4HpcidsVNsOelZUV7dv70r69LwsW/B8BAb9x7NhRTp06TnR0FN9++zXffvs19vYOdOrU\nhR49etGlSzcZPaGCSViUwfnzZwDo0qWbwpUIYZ6Kc0bSvLUvzVv7Mmvei1wPucrFC2c4e+ZnQkNv\n8tNPJ/jppxMA1K/fgI4dO+Pr24n27Tvi6upaCXtgOSQsSik6OoqIiDs4OjrKMB9ClFKJz0hU7jz9\nzExmzZxLTMw9zp49zZkzp7l06TciIu4QEXGHPXu+BaBJk6a0a9eB1q3b0qpVGzw8asv1jjKQsCil\nCxdyu6A6duyCtbUcRiEqm4dHbUaOHMPIkWPIzs4mJCSY3377lYsXLxAUFMjNmze4efMG3367A4Ba\ntdxo1aoNrVq1oUePztSq5YmDQzWF98J8yE+5Uvr5558A6YISwhRYW1sbgmDKlGlkZmby++9XuHIl\ngN9/D+L3368QHx/HyZPHOHnyGOvWgUqlwtOzHk2bNqNp02b4+OS+XF2rK707JknCohQSEx/w66/n\nsLKyonfvPkqXI4T4G1tbW3x9O+Hr2wnInXMmIuIOv/9+haCgQEJDr3Pjxg0iIyOIjIzg+PEjhrZu\nbu54ezfE27shXl4N8fLyxsurocVfA5GwKIUTJ46Sk5ND16495LcQIcyAWq3+44e+N0OHPoGbmxPR\n0QmEhd3ixo2QP17XuXnzOnFxscTFxXLhwrl8n+Hi4oq3d0MaNPDG09OTunU9qVu3HnXreuLoWPVv\nnZewKIUjRw4BMHDg4wpXIoTlKc0zHX99niOPjY2NoespT05ODtHRkYSHh3P7dji3b4f98Wc4SUmJ\nBARcIiDgUoHPd3Z2NgSHh0dt3N09cHd3x80t91W9eg3UavMeiq9YYaHValm1ahV79+4lOTmZ5s2b\ns2jRIrp3N/58QUxMDG+++SanT59Gp9PRrVs3li9fTv369Qusu3PnTr744gsiIyOpW7cu/v7+TJ48\nueR7VYEiIu4QGBiARmMrXVBCKKA0z3QU9jxHYaysrKhf34v69b3y/fvW6/XExsZy+3YYERF3uHs3\niqioKKKjo7h7N4rk5GSSk69y7drVIj83Lzjc3NypVasWrq41qFGjBtWrV8fVNffP6tWrY2trV6J9\nqyzFCotly5bx448/4u/vj5eXF7t372bGjBls3ryZDh06FNkuNTUVf39/UlNTmT17NtbW1mzcuBF/\nf3/27NmDi4uLYd3t27fz8ssv4+fnx7Rp0/j111959dVXyczM5Nlnny37npaTr776Asg9q5A7KYSw\nDCqVCg8PDzw8PArc1KLX60lIuE90dDTR0ZHExsYQGxtj6M6KjY0lKSmRe/fucu/eXaPbsrd3MASH\nq2sNXFxccHR0xMnJGUdHJ5yccl+Ojs44OTn+8acTtra2FbX7QDHCIjAwkP3797N8+XKmTp0KwKhR\noxg+fDgrV65ky5YtRbbdunUrt2/fZteuXbRsmTvXQ+/evXniiSfYuHEjCxcuBCAjI4MPP/yQAQMG\nsGrVKgDGjx+PTqdj7dq1jBs3Dicnp7Lua5nduXObw4cPYmVlhb+/6QSYEOLR/t51pU9II60YXVmF\ndV8V+GyVipo1a1GzZi3atGlb6DqZmRnEx8cbgiQh4T4PHjzgwYMEEhMfGP7+4EEC6elppKenER0d\nVaJ91GhscXR0pEePXixZsqJEbYvDaFgcPHgQGxsbxo0bZ1hma2vL2LFj+fDDD4mNjcXdvfDH7A8d\nOkT79u0NQQHQuHFjunfvzoEDBwxhce7cORITE5k0aVK+9pMnT+a7777j1KlTDBs2rFQ7WF5SUlJ4\n553X0el0PPHEKOrW9VS0HiFE8f2968rJ0Y6HKRlG2xW3++qvsnWQmfX3ILLGtVZtXGvVxqeQOdLy\nQkmv15OamsqDB3lh8oCHD5N4+PAhKSkP8/2Z/+/JaLWZJCRkcvnyb+j1+nJ/ANHoUQgODqZhw4ZU\nq5a/y6Vt27bo9XqCg4MLDQudTkdISAgTJkwo8F6bNm04ffo06enp2Nvbc/Vqbj9f69at863XqlUr\n1Go1V69eLXFYqNUlP1Dp6Wls2vQFDx4koNPp0Ov15OTkoNfriYuL5f79OFq3bsP06bOK9fnWVmoc\n7GyKtW17W2tysm1K3K402ypLm8rc1t/b/PUYmUJ9lbkt+S6Vb5tif5dsrMjMLtm84To9/B6WUKI2\n7ZrUQmNtBahwdnbC2dkJLy/vYrfX6/VotVpSU1NwdnbByqrkF9ON/UwzGhZxcXF4eHgUWO7m5gZA\nbGxsoe0SExPRarWG9f7eNvcHcBwNGjQgLi4OjUZT4D7mvGVFbeNRqlcvzfUER1asWFaKdkWrV8fF\n+EqFaFSv5LfkVlabytyWqddXmduS+iq/TWk1qFu6f/dlV7PCPtlo/GRkZGBjUzCB8y6mZGZmFtou\nb3lhQwjntc3IyHjkNvLWLWobQgghKofRsLCzsyMrK6vA8rwf4EVdgc9brtVqi2xrZ2dn+LOw9fLW\nreir/EIIIR7NaFi4ubkV2g0UF5d7saioi9uurq5oNBrDen9vq1KpDF1Ubm5uZGVlkZiYmG89rVZL\nYmJikdsQQghROYyGRfPmzQkLCyM1NTXf8oCAAMP7hX6wWo2Pjw9BQUEF3gsMDMTLywt7e3sAWrRo\nAVBg3aCgIHQ6neF9IYQQyjAaFn5+fmRlZbFz507DMq1Wy65du/D19TVc/I6OjiY0NDRf28GDB3P5\n8mXD3U4At27d4uzZs/j5+RmWdevWDVdXV7Zu3Zqv/bZt23BwcOCxxx4r3d4JIYQoFyq9Xq83ttLC\nhQs5evQozzzzDA0aNGD37t0EBQWxadMmOnbsCMCUKVM4f/48ISEhhnYpKSmMHj2a9PR0pk2bhpWV\nFRs3bkSv17Nnzx6qV//z7oQtW7bw6quv4ufnR69evfj111/Zs2cPixcvZsaMGRWw60IIIYqrWGGR\nmZnJRx99xHfffUdSUhLNmjXjxRdfpEePHoZ1CgsLgHv37uUbG6pr166sWLGi0LGhduzYYRgbqk6d\nOkyZMgV/f/9y2E0hhBBlUaywEEIIYdnMe8xcIYQQlULCQgghhFESFpUgMDCQ//znPwwdOpT27dvT\nt29fFi1axO3btwus+9tvvzFx4kTatWtHz549ef3110lPT1egauVt2LCBZs2aMXLkyALvWfJxCgwM\nZObMmXTu3JkOHTowYsQIdu3alW+do0ePMnr0aNq0aUPfvn1Zu3Yt2dklmzDIXIWHh/PCCy/w2GOP\n0b59e4YOHcr69esLPPhryd+h0rB65ZVXXlG6iKrujTfe4PTp0/Tr14/Ro0fTsGFDDh48yObNmxk0\naBA1atQAcgdtfPrpp3FxcWHWrFk0aNCA//3vf1y9epXhw4crvBeVKy4ujoULF2JjY4OLiwsTJ040\nvGfJx+nkyZM899xz1KlTh4kTJ/LYY4/h5OSEVqulS5cuhnXmzJlDkyZNmD59Oi4uLnz++eckJSXR\np0/VnrArJiaG0aNHG0axHjhwINnZ2WzcuJGoqCgefzx3dktL/g6Vml5UuIsXL+ozMzPzLQsLC9O3\nbt1av3TpUsOy6dOn63v37q1PSUkxLNuxY4fex8dH/8svv1RavaZg6dKl+ilTpuiffvpp/YgRI/K9\nZ6nHKTk5Wd+9e3f9a6+99sj1hg4dqh89erQ+OzvbsOyDDz7QN2/eXB8WFlbBVSrr008/1fv4+Oiv\nX7+eb/nzzz+vb9mypV6r1er1esv9DpWFdENVAl9f3wIDKnp7e9O0aVPDg4wpKSn88ssvjBo1Kt9w\n8CNHjsTBwYEDBw5Uas1KCgwMZN++fSxfvrzAe5Z8nL777juSk5MN88CkpKSg/9vNjDdv3uTmzZtM\nmDABKysrw/JJkyah0+n48ccfK7XmypY30kTNmvlHX61VqxbW1tZYWVlZ9HeoLCQsFKLX64mPjzc8\nmBgSEkJ2dnaBOT00Gg0tWrQgODhYiTIrnV6v57XXXmPUqFGFDvNiycfpzJkzNGrUiJMnT9KnTx86\nduxIly5dWLlyJTk5OQBFzg3j4eFB7dq1842mUBV17twZgBUrVnDt2jXu3r3Lvn37DFNBq9Vqi/4O\nlUXJpoAS5Wbfvn3ExMSwaNEi4M+BGYua/+Py5cuVWp9S9uzZw82bN/n4448Lfd+Sj9Pt27e5d+8e\ny5YtY/r06bRs2ZLjx4+zYcMGMjMzWbFihdHjU5q5YcxJr169WLhwIZ9++inHjh0zLF+wYAHz5s0D\nLPs7VBYSFgoIDQ3l1VdfpWPHjoY7ffLm9ihq/o+896uylJQU3n//fWbOnFnkSMOWfJzS0tJISkri\n//7v/5g5cyYAjz/+OGlpaWzbto05c+YYPT6WcLdPvXr16NKlC4MGDcLV1ZUTJ06wZs0aatSowcSJ\nEy36O1QWEhaVLC4ujlmzZuHi4sKqVatQq3N7AvPm9ihq/o+896uyTz75BBsbG6ZNm1bkOpZ8nPL2\n7e936zzxxBMcPHiQK1euWPTxAdi/fz8vv/wyBw8eNAxy+vjjj6PX63n33XcZOnSoxR+j0pJrFpXo\n4cOHzJgxg4cPH/LZZ5/lOw3O+3tR839U9Tk9YmNj2bRpE5MmTSI+Pp7IyEgiIyPJzMwkKyuLyMhI\nkpKSLPo45e17rVq18i3P+29LPz4AW7dupVWrVgWmgu7fvz9paWlcu3bN4o9RaUlYVJLMzExmz55N\neHg4n376KY0aNcr3vo+PD9bW1gXm9NBqtQQHB1f5OT3u379PVlYWK1euZMCAAYZXQEAAoaGhDBgw\ngA0bNlj0cWrVqhWQ+yzBX927dw+AGjVqFDk3TExMDPfu3avSxwcgPj7ecLH/r/Jm+8zJybHo71BZ\nSFhUgpycHF544QUuX77MqlWraN++fYF1nJyc6N69O3v37s030dTevXtJS0vLN/9HVVSvXj0+/vjj\nAq+mTZvi6enJxx9/zKhRoyz6OOXt2zfffGNYptfr2blzJw4ODrRv356mTZvSqFEjvv7663w/NLdt\n24ZarTY8lFZVNWzYkKCgIO7cuZNv+f79+7GysqJZs2YW/R0qCxl1thK88cYbfPXVV/Tr148hQ4bk\ne69atWoMHDgQgN9//52nnnqKpk2bMm7cOO7du8eXX35J165d2bBhgxKlK27KlCkkJyezd+9ewzJL\nPk5Lly5l7969jB07lpYtW3Ly5ElOnDjBP/7xD6ZPnw7A8ePHmTNnDt26dWPo0KFcv36dLVu2MGHC\nBKr6gA0XLlzgmWeeoXr16kyePBkXFxdOnDjBqVOneOqpp/jPf/4DWPZ3qLQkLCpB3lwfhfH09Mx3\ni9+vv/7KypUruXr1Ko6OjgwdOpQXX3wRBweHyirXpBQWFmC5x0mr1bJu3Tr27NlDfHw89erVY+rU\nqTz11FP51jty5Ahr164lNDSUGjVqMGbMGObOnYu1ddW/pyUwMJA1a9YQHBxMYmIinp6ejBkzhuee\ney7fg4qW+h0qLQkLIYQQRsk1CyGEEEZJWAghhDBKwkIIIYRREhZCCCGMkrAQQghhlISFEEIIoyQs\nhBBCGCVhIYQQwigJCyGEEEZJWAghhDBKwkIIIYRRVX9UMSHKWUpKChs2bOCXX37hzp07pKamUqdO\nHQYPHsy8efOwt7c3rPvgwQPeffddjh07hlarpU2bNixbtoy33nqLqKiofINIAly5coX//ve//Prr\nr6SmpuLp6cmoUaOYMWOGRQwCKEyXDCQoRAmFhobi7+/P448/jre3N9bW1pw/f55Dhw7Rs2dPPv/8\ncyB3hNjx48cTHBzMk08+SZs2bQgJCeHgwYO4uLiQnZ2dLyxOnDjB/Pnz8fLyYsSIEbi4uHD58mX2\n7t3LoEGDWL16tVK7LATohRAlkpmZqddqtQWWf/jhh3ofHx99QECAXq/X6//3v//pfXx89OvWrcu3\nXt7yfv36GZZlZGToe/TooZ80aZI+Kysr3/pffvml3sfHR3/27NkK2BshikeuWQhRQhqNBhsbGwCy\ns7NJSkoiISGBHj16ABAQEADkTkJkZWWFv79/vvbjxo3Dyckp37LTp08THx/Pk08+SXJyMgkJCYbX\nY489ZlhHCKVIJ6gQpbBlyxa2b9/OzZs30el0+d5LSkoCIDIyEnd3d6pVq5bvfY1GQ7169UhOTjYs\nCw0NBeCll14qcpvx8fHlVb4QJSZhIUQJffnll7z99tv06tULf39/3N3dsbGxISYmhmXLlqEvxWXA\nvDZLliyhRYsWha7j7u5eprqFKAsJCyFKaO/evXh6erJhwwbU6j97ck+dOpVvPU9PT86cOUNqamq+\ns4usrCwiIyNxdnY2LPP29gbA3t7e0J0lhCmRaxZClJBarUalUuU7g8jOzmbDhg351uvfvz85OTl8\n9dVX+Zbv2LGDhw8f5lvWq1cvatasyYYNG0hMTCywzYyMDFJSUspxL4QoGTmzEKKE/Pz8eP/995kx\nYwaDBg0iJSWF77//vsBzEOPGjWP79u189NFH3LlzJ9+ts15eXmRnZxvWdXBw4J133mHevHn4+fkx\nZswYvLy8SE5O5tatWxw+fJi1a9fStWvXyt5dIQB5zkKIEsvJyeGzzz7jm2++4e7du7i5uTFkyBDG\njBnD0KFDmT9/Ps8//zwACQkJ+R7Ka9u2LcuXL2fFihVkZGTwww8/5Pvs69evs379es6dO8eDBw9w\ndnamQYMG9O7dm8mTJ+Pq6qrELgshYSFEZcvJyaFbt260bdvW8ACfEKZOrlkIUYEyMjIKLNu+fTvJ\nycn07NlTgYqEKB25ZiFEBfrnP/+JVqulQ4cOaDQaLl26xPfff4+Xlxfjx49Xujwhik26oYSoQHv2\n7GHLli2Eh4eTlpZGSOpR4wAAAEZJREFUzZo16dOnDwsXLqRWrVpKlydEsUlYCCGEMEquWQghhDBK\nwkIIIYRREhZCCCGMkrAQQghhlISFEEIIoyQshBBCGPX/vWzJHqBuWbQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train['age'], bins=20, kde=False, fit=stats.gamma);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 892
    },
    "colab_type": "code",
    "id": "OFbZtvpR9c1i",
    "outputId": "cf03bff5-9bd6-4715-b655-ffa05cbb203e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HP/iBZs+erX79+kmSdu7cKZ/PpwsvvDDkcxISEnT++eeHrBDIy8tTr169QsK5JA0YMECm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2thYWFhYdHSafUZ+aqqKmbMmMHHH3+MYRgIITCMYGYq9LMQgt/97neHPVdhYSEpKSlh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\nryu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zePTRRwe6KAOrUvZziZi9zVc70jGNtf1mFNNATSRKWpw5bfekY/YPFYDg6Akl999T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\nL4WEdApTgKkF2Phhkg1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jplE14RTaHZwnKmZfwY8K6PQVGBkTMIvaK1VVaG5PceN9fT+L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P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CxXN5y20ERl2gtw8lu1CyonmlfOQt3iCZznZ8p+kK/B/f/m0aHliD2tFK+46HiP1jJ4Io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\nHFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1024.15x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all numeric variables against each other\n",
    "g = sns.pairplot(\n",
    "    train[list(\n",
    "        set(train.select_dtypes(include='number').columns)\n",
    "        - set(['education-num'])\n",
    "        ) + ['50k']],\n",
    "    hue='50k',\n",
    "    height=2.5,\n",
    "    aspect=1,\n",
    ")\n",
    "import numpy as np\n",
    "for i, j in zip(*np.triu_indices_from(g.axes, 1)):\n",
    "    g.axes[i, j].set_visible(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fWpX814__Gbg"
   },
   "source": [
    "We can observe variables with respect to each other. For example, we can ask if age could be one of the determiners of higher salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "colab_type": "code",
    "id": "x0r2_FTj88IS",
    "outputId": "1b5d734d-33c3-4b6d-9c7c-3a9ec678edde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fc5d5d2a630>"
      ]
     },
     "execution_count": 126,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='50k', y='age', kind='box', data=train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QCwS3nDTiycs"
   },
   "source": [
    "To get an idea of the redundancy between variables, we'll plot a correlation matrix based on Maximal Information-based Nonparametric Exploration ([Reshef et al, ScienceMag 2011](https://science.sciencemag.org/content/334/6062/1518.full)). This measure has been hailed as the correlation metric of the 21st century in science magazine. It ranges between 0, no correlation, and 1, perfect correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 212
    },
    "colab_type": "code",
    "id": "zafAJG3Bhkl6",
    "outputId": "f5e6bdd8-1574-4cd5-fd8d-1493161d4ba2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting minepy\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/90/a6/cdfe0f50b16d18196b3a21d3df8c06c361ccbd553717093133a88227823b/minepy-1.2.4.tar.gz (493kB)\n",
      "\u001b[K     |████████████████████████████████| 501kB 2.8MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.3.0 in /usr/local/lib/python3.6/dist-packages (from minepy) (1.17.4)\n",
      "Building wheels for collected packages: minepy\n",
      "  Building wheel for minepy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for minepy: filename=minepy-1.2.4-cp36-cp36m-linux_x86_64.whl size=174005 sha256=c9d4bc696ca9762ca2a202b6e9f1c4da60b2566e06ad4a574ef4491a7e82114d\n",
      "  Stored in directory: /root/.cache/pip/wheels/ea/ad/3a/0e6f5c87be5ee6ad987bd7a317dd6b92e616d559f63f4d8acc\n",
      "Successfully built minepy\n",
      "Installing collected packages: minepy\n",
      "Successfully installed minepy-1.2.4\n"
     ]
    }
   ],
   "source": [
    "pip install minepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "X8q3kfNBq5vA"
   },
   "outputs": [],
   "source": [
    "corrs = X_cleaned.corr(method='spearman').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NqA-M-tLr72y"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from sklearn.metrics.cluster import adjusted_mutual_info_score\n",
    "from minepy import MINE\n",
    "import multiprocessing\n",
    "\n",
    "def calc_mic(args):\n",
    "  (a, b, i1, i2) = args\n",
    "  mine = MINE(alpha=0.6, c=15, est='mic_approx')\n",
    "  mine.compute_score(a, b)\n",
    "  return (mine.mic(), i1, i2)\n",
    "\n",
    "pool = multiprocessing.Pool(os.cpu_count())\n",
    "\n",
    "corrs = np.zeros((len(X_cleaned.columns), len(X_cleaned.columns)))\n",
    "queue = []\n",
    "for i1, col1 in enumerate(X_cleaned.columns):\n",
    "  if i1 == 1:\n",
    "    continue\n",
    "  for i2, col2 in enumerate(X_cleaned.columns):\n",
    "    if i1 < i2:\n",
    "      continue\n",
    "    queue.append((X_cleaned[col1], X_cleaned[col2], i1, i2))\n",
    "\n",
    "results = pool.map(calc_mic, queue)\n",
    "\n",
    "for (mic, i1, i2) in results:\n",
    "  corrs[i1, i2] = mic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PTD9A7qO7zd3"
   },
   "outputs": [],
   "source": [
    "corrs = pd.DataFrame(\n",
    "    corrs,\n",
    "    columns=list(X_cleaned.columns),\n",
    "    index=list(X_cleaned.columns)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "colab_type": "code",
    "id": "0Gh8-75cc7S9",
    "outputId": "0919466d-1f1a-4cdd-8a35-eafcd2dbaeda"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c8cc0c240>"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s7e0L9qUIIUo/bR2NVb169VJ+B3KrXLlyobtJSUnB2Fj9uYjKlSvnmX7/WWUqGMTExJCQ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\nEECUAYFAIBBAlAGBQCAQAPwvYw8gnbRhQeYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = np.zeros_like(corrs, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "cmap = sns.diverging_palette(h_neg=220, h_pos=10, n=50, as_cmap=True)\n",
    "sns.set_context(\"notebook\", font_scale=1.1, rc={\"lines.linewidth\": 2.0})\n",
    "sns.heatmap(\n",
    "    corrs,\n",
    "    square=True,\n",
    "    mask=mask,\n",
    "    cmap=cmap, vmax=1.0, center=0.5,\n",
    "    linewidths=.5,\n",
    "    cbar_kws={\"shrink\": .5}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZhlzQTW4fomr"
   },
   "source": [
    "Most features are relatively uncorrelated, however there is one pair of variables that stands out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "VGxmjeNe7s7e",
    "outputId": "9539c102-9f0c-434b-b496-4b33bb952999"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9995095286140694"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs.loc['education-num', 'education']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "BLNBsHHufXh3",
    "outputId": "eb173879-effc-4afb-a087-dbf83f12eaa4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       " 10th            0.0\n",
       " 11th            0.0\n",
       " 12th            0.0\n",
       " 1st-4th         0.0\n",
       " 5th-6th         0.0\n",
       " 7th-8th         0.0\n",
       " 9th             0.0\n",
       " Assoc-acdm      0.0\n",
       " Assoc-voc       0.0\n",
       " Bachelors       0.0\n",
       " Doctorate       0.0\n",
       " HS-grad         0.0\n",
       " Masters         0.0\n",
       " Preschool       0.0\n",
       " Prof-school     0.0\n",
       " Some-college    0.0\n",
       "Name: education-num, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.groupby(by='education')['education-num'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VJ-X_gab8C38"
   },
   "source": [
    "Education-num and Education are exactly the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oJFWs2L5iMDH"
   },
   "source": [
    "The UCI description page mentions missing variables, however, we cannot see any missing values here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "jsp3qu0AhNHW",
    "outputId": "ec717259-0188-43ea-a8e2-cf04ae0f2efc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               False\n",
       "workclass         False\n",
       "fnlwgt            False\n",
       "education         False\n",
       "education-num     False\n",
       "marital-status    False\n",
       "occupation        False\n",
       "relationship      False\n",
       "race              False\n",
       "sex               False\n",
       "capital-gain      False\n",
       "capital-loss      False\n",
       "hours-per-week    False\n",
       "native-country    False\n",
       "50k               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "m8MDWRHc52Jb"
   },
   "source": [
    "For neural network training, for categorical variables, we have the choice of either using embeddings or feeding them as one-hot-encodings. Let us try one-hot-encodings for simplicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gMojSf5YmdAZ"
   },
   "outputs": [],
   "source": [
    "encoder = ce.OneHotEncoder(cols=list(X.select_dtypes(include='object').columns)[:])\n",
    "encoder.fit(X, train['50k'])\n",
    "X_cleaned = encoder.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "7rtYdAq66ixa",
    "outputId": "cb34414a-a9af-493a-8a7c-6f931bd0a1c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass_1', 'workclass_2', 'workclass_3', 'workclass_4',\n",
       "       'workclass_5', 'workclass_6', 'workclass_7', 'workclass_8',\n",
       "       'workclass_9',\n",
       "       ...\n",
       "       'native-country_33', 'native-country_34', 'native-country_35',\n",
       "       'native-country_36', 'native-country_37', 'native-country_38',\n",
       "       'native-country_39', 'native-country_40', 'native-country_41',\n",
       "       'native-country_42'],\n",
       "      dtype='object', length=108)"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cleaned.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "psL6J71N6YI6"
   },
   "outputs": [],
   "source": [
    "x_cleaned_cols = X_cleaned.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vav_7GL9ngjS",
    "outputId": "d5dbdea3-bec7-4f0e-ac26-0a687154684b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K', ' >50K'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['50k'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "grcDEp4BoZ9R"
   },
   "source": [
    "We are going to encode target values in two columns as 1 if present and 0 if not present. It is good to remember that the python truth values correspond to this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "cGxmeBo6oTsd",
    "outputId": "7b082909-6000-46b6-e764-a530c3857d79"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[int(val) for val in [True, False]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hhe11FlSn--V"
   },
   "outputs": [],
   "source": [
    "y = np.zeros((len(X_cleaned), 2))\n",
    "y[:, 0] = train['50k'].apply(lambda x: x == ' <=50K')\n",
    "y[:, 1] = train['50k'].apply(lambda x: x == ' >50K')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5eoapl8upLbW"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "standard_scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o0ueTgMjpYlN"
   },
   "outputs": [],
   "source": [
    "X_cleaned = standard_scaler.fit_transform(X_cleaned)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2L6t9sjTif-j"
   },
   "source": [
    "We are ready to start training. For good practice, we will save the dataset and our two encoders, the one-hot encoder and the standard scaler, so we can trace our changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E5EhLJGjjJP1"
   },
   "outputs": [],
   "source": [
    "X_cleaned.to_csv('adult_before_training.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "px2GGs1kjxwf"
   },
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "1bMU1wuvj4bX",
    "outputId": "17910b4d-a578-4317-a3e1-18be47c56e5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['adult_encoder.joblib']"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump([encoder, standard_scaler], 'adult_encoder.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rkfngbqAqKEc",
    "outputId": "4f4e4365-0d17-4d41-9e81-114372941d5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 108)"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_cleaned.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "-0erUSz1pyNd",
    "outputId": "0fe596da-3326-4d32-87fe-4206b9eb46aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_12 (Dense)             (None, 20)                2180      \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 2)                 42        \n",
      "=================================================================\n",
      "Total params: 2,222\n",
      "Trainable params: 2,222\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(20, activation='selu', input_dim=108))\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "model.compile(\n",
    "    optimizer='rmsprop',\n",
    "    loss='categorical_hinge',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xzQ1lvcxrxMF"
   },
   "source": [
    "To make this a bit more interesting, we will use a generator this time to feed in our data. We have a choice again - we can use any generator really (i.e. using a function with *yield*) and providing the *steps_per_epoch* parameter, or write a class inherits from *tensorflow.keras.utils.Sequence* that implements the following methods:\n",
    "* ***len()***, for the fit_generator function to know how much more data is to come. This corresponds to *steps_per_epoch* and is $\\text{len}(\\text{data}) // \\text{batch_size}$.\n",
    "* ***__getitem__***(), for the fit_generator to ask for the next batch\n",
    "*  ***on_epoch_end()***, to do some shuffling or other things at the end of an epoch - this is optional.\n",
    "\n",
    "For simplicity, we will take the former approach. If we had not done our preprocessing already, we could put it into this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "PHlRNv_rraGH",
    "outputId": "517c7b40-4ae0-4cdf-bdc7-3d00aad30fc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "3256/3256 [==============================] - 8s 3ms/step - loss: 0.4101 - acc: 0.8008\n",
      "Epoch 2/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3758 - acc: 0.8137\n",
      "Epoch 3/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3737 - acc: 0.8138\n",
      "Epoch 4/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3712 - acc: 0.8154\n",
      "Epoch 5/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3709 - acc: 0.8156\n",
      "Epoch 6/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3703 - acc: 0.8153\n",
      "Epoch 7/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3680 - acc: 0.8167\n",
      "Epoch 8/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3666 - acc: 0.8173\n",
      "Epoch 9/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3668 - acc: 0.8167\n",
      "Epoch 10/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3658 - acc: 0.8180\n",
      "Epoch 11/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3640 - acc: 0.8189\n",
      "Epoch 12/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3622 - acc: 0.8192\n",
      "Epoch 13/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3628 - acc: 0.8194\n",
      "Epoch 14/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3628 - acc: 0.8191\n",
      "Epoch 15/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3638 - acc: 0.8184\n",
      "Epoch 16/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3618 - acc: 0.8195\n",
      "Epoch 17/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3607 - acc: 0.8204\n",
      "Epoch 18/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3589 - acc: 0.8213\n",
      "Epoch 19/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3586 - acc: 0.8213\n",
      "Epoch 20/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3586 - acc: 0.8211\n",
      "Epoch 21/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3550 - acc: 0.8227\n",
      "Epoch 22/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3520 - acc: 0.8244\n",
      "Epoch 23/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3510 - acc: 0.8248\n",
      "Epoch 24/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3488 - acc: 0.8257\n",
      "Epoch 25/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3478 - acc: 0.8268\n",
      "Epoch 26/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3462 - acc: 0.8272\n",
      "Epoch 27/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3457 - acc: 0.8274\n",
      "Epoch 28/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3439 - acc: 0.8287\n",
      "Epoch 29/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3445 - acc: 0.8284\n",
      "Epoch 30/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3420 - acc: 0.8292\n",
      "Epoch 31/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3424 - acc: 0.8298\n",
      "Epoch 32/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3417 - acc: 0.8299\n",
      "Epoch 33/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3419 - acc: 0.8296\n",
      "Epoch 34/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3410 - acc: 0.8299\n",
      "Epoch 35/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3402 - acc: 0.8307\n",
      "Epoch 36/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3393 - acc: 0.8306\n",
      "Epoch 37/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3377 - acc: 0.8319\n",
      "Epoch 38/50\n",
      "3256/3256 [==============================] - 8s 2ms/step - loss: 0.3378 - acc: 0.8317\n",
      "Epoch 39/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3367 - acc: 0.8324\n",
      "Epoch 40/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3368 - acc: 0.8319\n",
      "Epoch 41/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3365 - acc: 0.8326\n",
      "Epoch 42/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3364 - acc: 0.8326\n",
      "Epoch 43/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3351 - acc: 0.8327\n",
      "Epoch 44/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3333 - acc: 0.8345\n",
      "Epoch 45/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3342 - acc: 0.8331\n",
      "Epoch 46/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3335 - acc: 0.8342\n",
      "Epoch 47/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3330 - acc: 0.8342\n",
      "Epoch 48/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3322 - acc: 0.8346\n",
      "Epoch 49/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3318 - acc: 0.8344\n",
      "Epoch 50/50\n",
      "3256/3256 [==============================] - 7s 2ms/step - loss: 0.3323 - acc: 0.8345\n"
     ]
    }
   ],
   "source": [
    "def adult_feed(X_cleaned, y, batch_size=10, shuffle=True):\n",
    "  def init_batches():\n",
    "    return (\n",
    "        np.zeros((batch_size, X_cleaned.shape[1])),\n",
    "        np.zeros((batch_size, y.shape[1]))\n",
    "        )\n",
    "  batch_x, batch_y = init_batches()\n",
    "  batch_counter = 0\n",
    "  while True:  # this is for every epoch\n",
    "    indexes = np.arange(X_cleaned.shape[0])\n",
    "    if shuffle == True:\n",
    "      np.random.shuffle(indexes)\n",
    "    for index in indexes:\n",
    "      batch_x[batch_counter, :] = X_cleaned[index, :]\n",
    "      batch_y[batch_counter, :] = y[index, :]\n",
    "      batch_counter += 1\n",
    "      if batch_counter >= batch_size:\n",
    "        yield (batch_x, batch_y)\n",
    "        batch_counter = 0\n",
    "        batch_x, batch_y = init_batches()\n",
    "\n",
    "history = model.fit_generator(\n",
    "    adult_feed(X_cleaned, y, 10),\n",
    "    steps_per_epoch=len(X_cleaned) // 10,\n",
    "    epochs=50\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DF5dFA9h1m2E"
   },
   "source": [
    "This time we are going to plot the training progress over epochs from the history instead of using tensorboard. We did not do validation, so we are only going to plot training loss and training accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "8jJksUO-04hN",
    "outputId": "3d68f0dd-9301-42a8-bf62-b550c34dff43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc5dd885dd8>"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sWFzpkoiIVEy8g+DQD8KUw+CJ63RVICKxVdY2gn1eIgEnLYQll8M394OJM2Hi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\nJOYUBCIiMacgEBGJuf8PnsdZdl4qRJgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('Model Training')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Accuracy', 'Loss'], loc='center left')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "18GH2yuD2Q4n"
   },
   "source": [
    "Now we are going to predict on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "GkpjtzHT2Gnr",
    "outputId": "7e9a2324-3e63-4008-d8c9-7611110092a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-12-10 22:10:44--  http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2003153 (1.9M) [application/x-httpd-php]\n",
      "Saving to: ‘adult.test’\n",
      "\n",
      "adult.test          100%[===================>]   1.91M  2.07MB/s    in 0.9s    \n",
      "\n",
      "2019-12-10 22:10:45 (2.07 MB/s) - ‘adult.test’ saved [2003153/2003153]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "FHiXvnYA2N03",
    "outputId": "b7a95a63-90c2-4367-c0fd-dbdff7da763e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>|1x3 Cross validator</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802.0</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814.0</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951.0</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323.0</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    age   workclass  ...  native-country      50k\n",
       "0  |1x3 Cross validator         NaN  ...             NaN      NaN\n",
       "1                    25     Private  ...   United-States   <=50K.\n",
       "2                    38     Private  ...   United-States   <=50K.\n",
       "3                    28   Local-gov  ...   United-States    >50K.\n",
       "4                    44     Private  ...   United-States    >50K.\n",
       "\n",
       "[5 rows x 15 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['age', 'workclass', 'fnlwgt',\n",
    "             'education', 'education-num',\n",
    "             'marital-status', 'occupation',\n",
    "             'relationship', 'race', 'sex',\n",
    "             'capital-gain', 'capital-loss',\n",
    "             'hours-per-week', 'native-country', '50k']\n",
    "\n",
    "test = pd.read_csv(\n",
    "    'adult.test',\n",
    "    names=cols\n",
    ")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "GoUfyUbN3dPu",
    "outputId": "c467121f-3e8e-434c-b2b1-cfa0299d88e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        14\n",
       "1         0\n",
       "2         0\n",
       "3         0\n",
       "4         0\n",
       "         ..\n",
       "16277     0\n",
       "16278     0\n",
       "16279     0\n",
       "16280     0\n",
       "16281     0\n",
       "Length: 16282, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Tvc5I6W93pHy",
    "outputId": "85d1b8e1-1bbf-42ca-eeb4-818d6e112832"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.isnull().sum(axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Cq6N8vsp3wUe",
    "outputId": "08df2dfe-5cc4-4c44-b009-86d388a1d4af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 83,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZFyNFGGF3sJG"
   },
   "source": [
    "The first row has 14 nulls and 1 unusable column of 15 columns. We will discard this row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WaOG-Piz3q6l"
   },
   "outputs": [],
   "source": [
    "test.drop(0, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E7oUcl7e2ZsL"
   },
   "outputs": [],
   "source": [
    "X_test = standard_scaler.transform(encoder.transform(test[cols[:-1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jHcXqC2i4TBB"
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1UNxVM6V5qUj"
   },
   "source": [
    "We will calculate the AUC score using sklearn's inbuilt functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "obKwo2HS4X77"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "bVvlpQRf5ZLT",
    "outputId": "7078a30e-0029-4a3e-fa0d-a04f59d04d74"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' <=50K.', ' >50K.'], dtype=object)"
      ]
     },
     "execution_count": 95,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['50k'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "95RDmgAC5dbC"
   },
   "source": [
    "Please note that the targets have slightly different names in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MVbMgUAk5i-b"
   },
   "outputs": [],
   "source": [
    "target_lookup = {' <=50K.': 0, ' >50K.': 1 }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "vdVQn3hp4o6d",
    "outputId": "5d7bfb3d-1825-4069-a05a-ed64d40fbe05"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.754459789971816"
      ]
     },
     "execution_count": 129,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = test['50k'].apply(\n",
    "    lambda x: target_lookup[x]\n",
    ").values\n",
    "roc_auc_score(y_test, predictions.argmax(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Qk8uvplNR4WW"
   },
   "source": [
    "Finally, we are going to check the feature importances of our variables. We are going to use the eli5 package for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "colab_type": "code",
    "id": "dPFiS5ZsA4Xr",
    "outputId": "f81e7cf5-6e00-408f-b6bd-971a9ea803ed"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting eli5\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/97/2f/c85c7d8f8548e460829971785347e14e45fa5c6617da374711dec8cb38cc/eli5-0.10.1-py2.py3-none-any.whl (105kB)\n",
      "\r",
      "\u001b[K     |███                             | 10kB 19.9MB/s eta 0:00:01\r",
      "\u001b[K     |██████▏                         | 20kB 1.8MB/s eta 0:00:01\r",
      "\u001b[K     |█████████▎                      | 30kB 2.6MB/s eta 0:00:01\r",
      "\u001b[K     |████████████▍                   | 40kB 1.7MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████▌                | 51kB 2.1MB/s eta 0:00:01\r",
      "\u001b[K     |██████████████████▋             | 61kB 2.5MB/s eta 0:00:01\r",
      "\u001b[K     |█████████████████████▊          | 71kB 2.9MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████▊       | 81kB 3.3MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████████▉    | 92kB 3.7MB/s eta 0:00:01\r",
      "\u001b[K     |███████████████████████████████ | 102kB 2.8MB/s eta 0:00:01\r",
      "\u001b[K     |████████████████████████████████| 112kB 2.8MB/s \n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from eli5) (1.17.4)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from eli5) (1.3.3)\n",
      "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from eli5) (0.21.3)\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from eli5) (0.10.1)\n",
      "Requirement already satisfied: jinja2 in /usr/local/lib/python3.6/dist-packages (from eli5) (2.10.3)\n",
      "Requirement already satisfied: attrs>16.0.0 in /usr/local/lib/python3.6/dist-packages (from eli5) (19.3.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from eli5) (1.12.0)\n",
      "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.6/dist-packages (from eli5) (0.8.6)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->eli5) (0.14.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2->eli5) (1.1.1)\n",
      "Installing collected packages: eli5\n",
      "Successfully installed eli5-0.10.1\n"
     ]
    }
   ],
   "source": [
    "pip install eli5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eQXGZrlSBRc"
   },
   "source": [
    "Eli5 can calculate the permutation importance, which measures the increase in the prediction error when feature values are permuted. For this to work we need a scoring function as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8Yp9K6WmONA3"
   },
   "outputs": [],
   "source": [
    "from eli5.permutation_importance import get_score_importances\n",
    "\n",
    "def score(data, y=None, weight=None):\n",
    "  return model.predict(data).argmax(axis=1)\n",
    "\n",
    "base_score, score_decreases = get_score_importances(score, X_test, y_test)\n",
    "feature_importances = np.mean(score_decreases, axis=0).mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "U6o_CmjbPAY8",
    "outputId": "97fa2bee-f54c-4972-c07b-ea532b54c0b3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age: 0.026988514219028318\n",
      "marital-status_2: 0.017492782998587312\n",
      "relationship_4: 0.014286591732694552\n",
      "marital-status_1: 0.011915729992015232\n",
      "education_1: 0.011792887414777962\n",
      "hours-per-week: 0.009471162704993551\n",
      "relationship_1: 0.009274614581413917\n",
      "education-num: 0.00793563048952767\n",
      "occupation_5: 0.007874209200909034\n",
      "relationship_2: 0.007505681469197223\n",
      "sex_2: 0.006682636201707512\n",
      "relationship_3: 0.006633499170812604\n",
      "sex_1: 0.004729439223634912\n",
      "education_4: 0.0033536023585774826\n",
      "occupation_2: 0.003009643142313126\n",
      "education_6: 0.0027025366992199495\n",
      "marital-status_3: 0.0026411154106013145\n",
      "race_1: 0.0026288311528775876\n",
      "workclass_5: 0.0022848719366132305\n",
      "occupation_3: 0.002198882132547141\n",
      "education_9: 0.001928628462625146\n",
      "occupation_1: 0.0019040599471776918\n",
      "marital-status_7: 0.0018917756894539651\n",
      "education_11: 0.0017566488544929672\n",
      "relationship_6: 0.0017443645967692403\n",
      "occupation_10: 0.001646090534979424\n",
      "occupation_11: 0.001609237761808243\n",
      "occupation_12: 0.001609237761808243\n",
      "occupation_13: 0.0016092377618082427\n",
      "workclass_7: 0.0013512683496099747\n",
      "occupation_4: 0.0012038572569252504\n",
      "workclass_4: 0.001105583195135434\n",
      "relationship_5: 0.0010687304219642528\n",
      "capital-loss: 0.0010687304219642528\n",
      "education_5: 0.0009704563601744365\n",
      "education_7: 0.000847613782937166\n",
      "education_13: 0.0007739082365948039\n",
      "workclass_6: 0.0007370554634236226\n",
      "workclass_1: 0.000577360113015171\n",
      "fnlwgt: 0.0005650758552914441\n",
      "occupation_9: 0.0004668017935016277\n",
      "marital-status_4: 0.00045451753577790065\n",
      "education_12: 0.0004176647626067195\n",
      "native-country_7: 0.0003685277317118113\n",
      "native-country_24: 0.0003562434739880843\n",
      "native-country_16: 0.00035624347398808425\n",
      "education_16: 0.0003316749585406302\n",
      "native-country_30: 0.00031939070081690317\n",
      "native-country_20: 0.0003193907008169031\n",
      "race_4: 0.0003071064430931761\n",
      "native-country_36: 0.00025796941219826785\n",
      "native-country_13: 0.00020883238130335976\n",
      "native-country_32: 0.00020883238130335976\n",
      "native-country_40: 0.00019654812357963267\n",
      "native-country_35: 0.00018426386585590566\n",
      "native-country_38: 0.00018426386585590566\n",
      "native-country_34: 0.00017197960813217865\n",
      "marital-status_5: 0.00017197960813217862\n",
      "native-country_25: 0.00017197960813217854\n",
      "education_14: 0.00015969535040845164\n",
      "native-country_33: 0.0001596953504084516\n",
      "native-country_27: 0.00015969535040845153\n",
      "native-country_3: 0.00013512683496099748\n",
      "occupation_15: 0.0001228425772372703\n",
      "native-country_18: 0.0001105583195135434\n",
      "native-country_19: 0.00011055831951354337\n",
      "native-country_23: 0.00011055831951354333\n",
      "native-country_26: 9.827406178981639e-05\n",
      "education_3: 8.59898040660893e-05\n",
      "workclass_8: 7.370554634236229e-05\n",
      "native-country_21: 7.370554634236229e-05\n",
      "native-country_31: 7.370554634236227e-05\n",
      "native-country_28: 7.370554634236219e-05\n",
      "education_10: 6.142128861863521e-05\n",
      "native-country_41: 4.913703089490818e-05\n",
      "education_8: 4.9137030894908086e-05\n",
      "workclass_9: 3.685277317118115e-05\n",
      "native-country_22: 2.4568515447454083e-05\n",
      "native-country_37: 1.2284257723727055e-05\n",
      "native-country_39: 1.2284257723727028e-05\n",
      "native-country_42: 0.0\n",
      "occupation_14: -3.409566441327795e-21\n",
      "native-country_9: -2.4568515447454097e-05\n",
      "native-country_10: -3.685277317118122e-05\n",
      "marital-status_6: -0.00012284257723727045\n",
      "education_15: -0.00022111663902708688\n",
      "native-country_17: -0.0002579694121982679\n",
      "occupation_6: -0.00030710644309317594\n",
      "native-country_2: -0.00031939070081690317\n",
      "race_5: -0.0003316749585406302\n",
      "native-country_4: -0.00039309624715926544\n",
      "native-country_29: -0.000565075855291444\n",
      "native-country_15: -0.0005650758552914441\n",
      "race_3: -0.0007739082365948039\n",
      "native-country_11: -0.0007984767520422579\n",
      "native-country_12: -0.0009581721024507095\n",
      "workclass_3: -0.0011178674528591608\n",
      "education_2: -0.0013758368650574288\n",
      "native-country_8: -0.001388121122781156\n",
      "native-country_14: -0.001400405380504883\n",
      "occupation_7: -0.0015723849886370618\n",
      "native-country_1: -0.001682943308150605\n",
      "native-country_5: -0.0017320803390455134\n",
      "race_2: -0.002530557091087771\n",
      "occupation_8: -0.002542841348811498\n",
      "workclass_2: -0.0025919783797064065\n",
      "native-country_6: -0.0032061912658927585\n",
      "capital-gain: -0.0033413181008537566\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "\n",
    "feature_importances_annotated = {col: imp for col, imp in zip(x_cleaned_cols, feature_importances)}\n",
    "sorted_feature_importances_annotated = sorted(feature_importances_annotated.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\n",
    "for k, v in sorted_feature_importances_annotated:\n",
    "  print('{k}: {v}'.format(k=k, v=v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FzRq8sN59aSn"
   },
   "source": [
    "So, yes, as expected, age is a significant factor."
   ]
  }
 ],
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   "name": "Modeling and Predicting in Keras",
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